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death england estimate excess wale weekly estimation unite coverage level march provisional adjust english july

Using a delay-adjusted case fatality ratio to estimate under-reporting
March 22, 2020 · · Original resource · preprint

Aim To estimate the percentage of symptomatic COVID-19 cases reported in different countries using case fatality ratio estimates based on data from the ECDC, correcting for delays between confirmation-and-death.
covid-19
data
hospitalization
modeling
mortality
recovery
reporting
symptomatic
estimate
code
death, england, estimate, excess, wale
patient, hydroxychloroquine, cohort, mortality, observational
Using a delay-adjusted case fatality ratio to estimate under-reporting
March 22, 2020 · · Original resource · preprint

Aim To estimate the percentage of symptomatic COVID-19 cases reported in different countries using case fatality ratio estimates based on data from the ECDC, correcting for delays between confirmation-and-death.
covid-19
data
modeling
prediction
mortality
reporting
fatality
symptomatic
code
death, england, estimate, excess, wale
patient, hydroxychloroquine, cohort, mortality, observational
Estimation of Excess Deaths Associated With the COVID-19 Pandemic in the United States, March to May 2020

Importance  Efforts to track the severity and public health impact of coronavirus disease 2019 (COVID-19) in the United States have been hampered by state-level differences in diagnostic test availability, differing strategies for prioritization of individuals for testing, and delays between testing and reporting. Evaluating unexplained increases in deaths due to all causes or attributed to nonspecific outcomes, such as pneumonia and influenza, can provide a more complete picture of the burden of COVID-19.Objective  To estimate the burden of all deaths related to COVID-19 in the United States from March to May 2020.Design, Setting, and Population  This observational study evaluated the numbers of US deaths from any cause and deaths from pneumonia, influenza, and/or COVID-19 from March 1 through May 30, 2020, using public data of the entire US population from the National Center for Health Statistics (NCHS). These numbers were compared with those from the same period of previous years. All data analyzed were accessed on June 12, 2020.Main Outcomes and Measures  Increases in weekly deaths due to any cause or deaths due to pneumonia/influenza/COVID-19 above a baseline, which was adjusted for time of year, influenza activity, and reporting delays. These estimates were compared with reported deaths attributed to COVID-19 and with testing data.Results  There were approximately 781 000 total deaths in the United States from March 1 to May 30, 2020, representing 122 300 (95% prediction interval, 116 800-127 000) more deaths than would typically be expected at that time of year. There were 95 235 reported deaths officially attributed to COVID-19 from March 1 to May 30, 2020. The number of excess all-cause deaths was 28% higher than the official tally of COVID-19–reported deaths during that period. In several states, these deaths occurred before increases in the availability of COVID-19 diagnostic tests and were not counted in official COVID-19 death records. There was substantial variability between states in the difference between official COVID-19 deaths and the estimated burden of excess deaths.Conclusions and Relevance  Excess deaths provide an estimate of the full COVID-19 burden and indicate that official tallies likely undercount deaths due to the virus. The mortality burden and the completeness of the tallies vary markedly between states.
covid-19
big data
usa
excess mortality
cause of death
research
estimation
cohort study
diagnostics
case reporting
death, england, estimate, excess, wale
patient, hydroxychloroquine, cohort, mortality, observational
Deaths involving COVID-19, England and Wales: deaths occurring in March 2020
April 16, 2020 · · Original resource · article

Number of deaths registered each month in England and Wales, including deaths involving the coronavirus (COVID-19), by age, sex and region.
covid-19
statistics
region
age
england
death rate
sex
wales
2020
death, england, estimate, excess, wale
hotspot, u.s, rise, record, come
Excess Deaths From COVID-19 and Other Causes, March-July 2020

Previous studies of excess deaths (the gap between observed and expected deaths) during the coronavirus disease 2019 (COVID-19) pandemic found that publicly reported COVID-19 deaths underestimated the full death toll, which includes documented and undocumented deaths from the virus and non–COVID-19 deaths caused by disruptions from the pandemic.1,2 A previous analysis found that COVID-19 was cited in only 65% of excess deaths in the first weeks of the pandemic (March-April 2020); deaths from non–COVID-19 causes (eg, Alzheimer disease, diabetes, heart disease) increased sharply in 5 states with the most COVID-19 deaths.1 This study updates through August 1, 2020, the estimate of excess deaths and explores temporal relationships with state reopenings (lifting of coronavirus restrictions).
covid-19
usa
transmission
official data
longitudinal change
epidemiology
modeling
excess mortality
cause of death
loosening restrictions
region
death, england, estimate, excess, wale
mobility, crime, gdp, employment, restriction
Deaths registered weekly in England and Wales, provisional: week ending 26 June 2020
July 7, 2020 · · Original resource · webpage

Provisional counts of the number of deaths registered in England and Wales, including deaths involving the coronavirus (COVID-19) pandemic, by age, sex and region, in the latest weeks for which data are available.
covid-19
big data
uk
case number
region
age
england
death rate
sex
wales
death, england, estimate, excess, wale
hotspot, u.s, rise, record, come
Deaths registered weekly in England and Wales, provisional: week ending 17 July 2020
July 28, 2020 · · Original resource · webpage

Provisional counts of the number of deaths registered in England and Wales, including deaths involving the coronavirus (COVID-19) pandemic, by age, sex and region, in the latest weeks for which data are available.
covid-19
uk
data
age
england
deaths
wales
death, england, estimate, excess, wale
hotspot, u.s, rise, record, come
Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

BackgroundThe risk of severe COVID-19 if an individual becomes infected is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 and how this varies between countries should inform the design of possible strategies to shield or vaccinate those at highest risk.MethodsWe estimated the number of individuals at increased risk of severe disease (defined as those with at least one condition listed as “at increased risk of severe COVID-19” in current guidelines) by age (5-year age groups), sex, and country for 188 countries using prevalence data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 and UN population estimates for 2020. The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. To help interpretation of the degree of risk among those at increased risk, we also estimated the number of individuals at high risk (defined as those that would require hospital admission if infected) using age-specific infection–hospitalisation ratios for COVID-19 estimated for mainland China and making adjustments to reflect country-specific differences in the prevalence of underlying conditions and frailty. We assumed males were twice at likely as females to be at high risk. We also calculated the number of individuals without an underlying condition that could be considered at increased risk because of their age, using minimum ages from 50 to 70 years. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 95% confidence limits for country population size, disease prevalences, multimorbidity fractions, and infection–hospitalisation ratios, and plausible low and high estimates for the degree of clustering, informed by multimorbidity studies.FindingsWe estimated that 1·7 billion (UI 1·0–2·4) people, comprising 22% (UI 15–28) of the global population, have at least one underlying condition that puts them at increased risk of severe COVID-19 if infected (ranging from <5% of those younger than 20 years to >66% of those aged 70 years or older). We estimated that 349 million (186–787) people (4% [3–9] of the global population) are at high risk of severe COVID-19 and would require hospital admission if infected (ranging from <1% of those younger than 20 years to approximately 20% of those aged 70 years or older). We estimated 6% (3–12) of males to be at high risk compared with 3% (2–7) of females. The share of the population at increased risk was highest in countries with older populations, African countries with high HIV/AIDS prevalence, and small island nations with high diabetes prevalence. Estimates of the number of individuals at increased risk were most sensitive to the prevalence of chronic kidney disease, diabetes, cardiovascular disease, and chronic respiratory disease.InterpretationAbout one in five individuals worldwide could be at increased risk of severe COVID-19, should they become infected, due to underlying health conditions, but this risk varies considerably by age. Our estimates are uncertain, and focus on underlying conditions rather than other risk factors such as ethnicity, socioeconomic deprivation, and obesity, but provide a starting point for considering the number of individuals that might need to be shielded or vaccinated as the global pandemic unfolds.
covid-19
modeling
age
risk factor
underlying health condition
open access
estimate
at risk
death, england, estimate, excess, wale
patient, hydroxychloroquine, cohort, mortality, observational
Estimates of the severity of coronavirus disease 2019: a model-based analysis

BackgroundIn the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases.MethodsWe collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation.FindingsUsing data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2) and to hospital discharge to be 24·7 days (22·9–28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56–3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23–1·53), with substantially higher ratios in older age groups (0·32% [0·27–0·38] in those aged <60 years vs 6·4% [5·7–7·2] in those aged ≥60 years), up to 13·4% (11·2–15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4–3·5] in those aged <60 years [n=360] and 4·5% [1·8–11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39–1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0–37·6) in those aged 80 years or older.InterpretationThese early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death.
covid-19
infection
data
china
hospitalization
government
mortality
age
research
fatality
death, england, estimate, excess, wale
patient, hydroxychloroquine, cohort, mortality, observational
Estimating the burden of SARS-CoV-2 in France

France has been heavily affected by the SARS-CoV-2 epidemic and went into lockdown on the 17 March 2020. Using models applied to hospital and death data, we estimate the impact of the lockdown and current population immunity. We find 3.6% of infected individuals are hospitalized and 0.7% die, ranging from 0.001% in those <20 years of age (ya) to 10.1% in those >80ya. Across all ages, men are more likely to be hospitalized, enter intensive care, and die than women. The lockdown reduced the reproductive number from 2.90 to 0.67 (77% reduction). By 11 May 2020, when interventions are scheduled to be eased, we project 2.8 million (range: 1.8–4.7) people, or 4.4% (range: 2.8–7.2) of the population, will have been infected. Population immunity appears insufficient to avoid a second wave if all control measures are released at the end of the lockdown.
covid-19
infection
immunity
lockdown
modeling
second wave
france
mortality
gender
intervention
death
impact
data analysis
men
hospital
death, england, estimate, excess, wale
transmission, cov-2, secondary, china, household